Physical activity and gestational weight gain predict physiological and perceptual responses to exercise during pregnancy
Bibliographic record
Abstract
Abstract Background Exercise is known to improve the health of the pregnant woman and her child. Studies that have evaluated physiological parameters during prenatal exercise have conflicting results. Better understanding of these physiological responses can modify exercise prescriptions, safety, and monitoring strategies. We examined the association between age, prepregnancy body mass index (BMI), gestational weight gain (GWG), and physical activity (PA) levels, factors that may influence a change in physiological (HR, VO2 responses) and perceptual (RPE) responses to acute exercise throughout pregnancy. Methods Twenty‐two healthy pregnant women (31.4 ± 3.7 years) performed a Submaximal incremental Walking Exercise Test (SWET). Early‐ (13–18 weeks), mid‐ (24–28 weeks), and late‐pregnancy (34–37 weeks) were compared. VO2 (L/min; ml/kg/min), HR (bpm), and RPE were collected at the end of each test stage. PA was determined by accelerometry. We associated PA levels, GWG, prepregnancy BMI, and age with HR, RPE, and VO2 responses. Results HR, RPE, and absolute VO2 were higher in late‐pregnancy compared to earlier time points (p < .05; η2 = 0.299–0.525). Regression models were built for HR (all time points), RPE (early‐ and late‐pregnancy), and VO2 (L/min; late‐pregnancy). HR (late‐pregnancy) was predicted by time in vigorous PA, GWG, age, and prepregnancy BMI (r2 = 0.645; SEE = 5.84). RPE (late‐pregnancy) was predicted by sedentary time, GWG, prepregnancy BMI, and age (r2 = 0.662; SEE = 1.21). Conclusion Physiological/perceptual responses were higher in late‐pregnancy compared to other time points and associated with combined PA, GWG, prepregnancy BMI, and age. These findings can be used to modify exercise prescriptions and designs for future PA interventions in pregnant women.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".